The 3V's of Big Data
Much of the tech industry follows Gartner's '3Vs' model to define Big Data. Data that is high in:
The volume of data organisations handle can progress from megabytes through to terabytes and even petabytes. In terms of velocity, data has gone from being handled in batches and periodically to having to be processed in real time. The variety of data has also diversified from simple tables and databases through to photo, web, mobile and social data, and the most challenging: unstructured data.
How big is 'Big Data'?
Every day, we create 2.5 quintillion bytes of data - so much, that 90% of data in the world today has been created in the last two years alone.
When data sets get so big that they cannot be analysed by traditional data processing application tools, it becomes known as 'Big Data'.
As different companies have varied ceilings on how much data they can handle, depending on their database management tools, there is no set level where data becomes 'big'.
This means that Big Data and analytics tend to go hand-in-hand, as without being able to analyse the data it becomes meaningless.
How to analyse Big Data
In today's dynamic business environment, analytics can become very complex; aggregating data from multiple projects and applications and in various different formats into a single set for analysis is an intricate challenge. This is why single analytics platforms that are embedded within core operational systems are growing in popularity; they ensure key data are integrated, not stored in silos where their insights are lost.
There are three key areas of analytics to consider when handling Big Data, according to Lori Ellsworth, president of Changepoint:
- Business analytics use fact-based data to measure past performance and stop small problems becoming big problems.
- Customer analytics examine customer conversations to identify trends, gaps and capture sentiment; for example, on social media.
- Consumption analytics track consumption of products and services; determining areas leading or lagging in adoption, or the use of product features.
Once identified, the second aspect of using analytics to transform a business is to determine the approach to these individual analytics groups. There are two high-level approaches to analytics: real-time and batch. Real-time analytics perform analysis on live data, for example, a services project that is currently in progress, detecting issues or problems as they happen, allowing them to be rectified before they negatively impact the project. Batch analytics are generally used to identify trends over time or for historical analysis, with one or more data sets scheduled for analysis at a set time. Both these methods have their own advantages and uses in different contexts and their use is determined by the needs of the business.
Ellsworth says: "Services businesses must ensure that all members have access to the right technologies that ensure analytics becomes part of their everyday skill-set. In doing this, services teams will have a better method to achieve customer satisfaction, and to capitalise on revenue opportunities."